Large-scale deployment of bioenergy plantations would have adverse
effects on water resources. There is an increasing need to ensure the
appropriate inclusion of the bioenergy crops in global hydrological models.
Here, through parameter calibration and algorithm improvement, we enhanced
the global hydrological model H08 to simulate the bioenergy yield from two
dedicated herbaceous bioenergy crops: Miscanthus and switchgrass. Site-specific
evaluations showed that the enhanced model had the ability to simulate yield
for both Miscanthus and switchgrass, with the calibrated yields being well within the
ranges of the observed yield. Independent country-specific evaluations
further confirmed the performance of the H08 (v.bio1). Using this improved
model, we found that unconstrained irrigation more than doubled the yield
under rainfed condition, but reduced the water use efficiency (WUE) by
32 % globally. With irrigation, the yield in dry climate zones can exceed
the rainfed yields in tropical climate zones. Nevertheless, due to the low
water consumption in tropical areas, the highest WUE was found in tropical
climate zones, regardless of whether the crop was irrigated. Our enhanced
model provides a new tool for the future assessment of bioenergy–water
tradeoffs.
Introduction
Bioenergy with carbon capture and storage technology enables the production
of energy without carbon emissions while sequestering carbon dioxide from
the atmosphere, producing negative emissions. Therefore, bioenergy is
considered an important technology in the push to achieve the 2∘C
climate target (Smith et al., 2015). With ambitious climate policies, the
demand for bioenergy in 2100 could reach 200–400 EJ per year, based on
recent predictions (Rose et al., 2013; Bauer et al., 2018). However,
large-scale planting of bioenergy crops requires water consumption to be
doubled or even tripled, which would exacerbate the future water scarcity
(Beringer et al., 2011; Bonsch et al., 2016; Hejazi et al., 2015; Yamagata
et al., 2018). Therefore, representation of bioenergy crops in global
hydrological models is critical in elucidating the possible side effects of
large-scale implementation of bioenergy.
Second-generation bioenergy crops, such as Miscanthus and switchgrass, are generally
regarded as a dedicated bioenergy source due to their high yield potential
and lack of direct competition with food production (Beringer et al., 2011;
Yamagata et al., 2018; Wu et al., 2019). This is because Miscanthus and switchgrass
are rhizomatous perennial C4 grasses, which have a high photosynthesis
efficiency (Trybula et al., 2015). These two crops have been included in a
series of models including Lund–Potsdam–Jena managed Land (LPJmL) (Beringer et al., 2011; Bondeau et al., 2007), H08 (Yamagata et al., 2018),
ORCHIDEE (Li et al., 2018b), the High-Performance Computing Environmental
Policy Integrated Climate model (HPC-EPIC) (Kang et al., 2014; Nichols et
al., 2011), the Community Land Model (version 5) (CLM5) (Cheng et al.,
2020), MISCANMOD (Clifton-Brown et al., 2000, 2004), MISCANFOR (Hastings et
al., 2009), Agricultural Production Systems Simulator (APSIM) (Ojeda et al.,
2017), and the Soil & Water Assessment Tool (SWAT) (Trybula et al.,
2015). However, among these models, only a few, such as LPJmL, H08, and CLM5,
include the global implementation of schemes for irrigation, river routing, or water withdrawal. This severely limits the application of the models to
address the global bioenergy–water tradeoffs or synergies.
To the best of our knowledge, LPJmL was the first global model that included
both bioenergy and the water cycle. It has therefore been widely used to
quantify the effects on water of large-scale planting of bioenergy crops
in many previous studies (Beringer et al., 2011; Heck et al., 2016, 2018;
Bonsch et al., 2016; Jans et al., 2018; Stenzel et al., 2019). However, it
should be noted that Miscanthus and switchgrass are not distinguished in LPJmL, which
instead uses a C4 grass to parameterize them. A separate parametrization for
the two bioenergy crops could enhance the bioenergy simulation since they
showed totally different plant characteristics and crop yield (Heaton et
al., 2008; Trybula et al., 2015; Li et al., 2018b). CLM5 has been improved
and validated for simulating Miscanthus and switchgrass separately based on
observations at the University of Illinois Energy Farm (Cheng et al., 2020),
but a global validation or application has not been reported. H08 is a
global hydrological model that considers human activities, including
reservoir operation, aqueduct water transfer, seawater desalination, and
water abstraction for irrigation, industry, and municipal use (Hanasaki et
al., 2008a, b, 2010, 2018a, b). The first use of H08 to simulate
the bioenergy crop yield was reported in an impact assessment of the effects
of bioenergy production on water, land, and ecosystem services (Yamagata et al., 2018).
Using an identical model to that of Yamagata et al. (2018), another recent
study also used H08 estimates of Miscanthus and switchgrass yield to predict global
advanced bioenergy potential (Wu et al., 2019). Based on the work of
Yamagata et al. (2018), here we improved the bioenergy crop simulation in
H08 by performing a systematic parameter calibration for both Miscanthus and
switchgrass using the best available data.
The objective of this study was to enhance and validate the ability of H08
to simulate the second-generation herbaceous bioenergy crop yield. The
following sections of this paper will (1) describe the default biophysical
process of the crop module in H08, (2) explain the enhancement of H08 for
Miscanthus and switchgrass, (3) evaluate the enhanced performance of the model in
simulating yields for Miscanthus and switchgrass, (4) map the spatial distributions of
the yield of Miscanthus and switchgrass, and (5) illustrate the effects of irrigation on
the yield, water consumption, and WUE (water use efficiency, defined here as the ratio of yield to
water consumption) of Miscanthus and switchgrass.
Materials and methodsH08 and its crop module
H08 is a global hydrological model that can simulate the basic natural and
anthropogenic hydrological processes as well as crop growth at a spatial
resolution of 0.5∘ and at a daily interval (Hanasaki et al.,
2008a, b). Main variables related to the water cycle, such as river
discharge, terrestrial water storage, and water withdrawal have been
thoroughly validated in a series of previous studies (Hanasaki et al.,
2008a, b, 2018a). H08 consists of six submodules. The six submodules
(land surface hydrology, river routing, crop growth, reservoir operation,
environmental flow requirements, and anthropogenic water withdrawal) are
coupled in a unique way (Fig. 1a). The land surface module can simulate the
main water cycle components, such as evapotranspiration and runoff. The
former is used in the crop module, and the latter is used in the river
routing and environmental flow modules. The agricultural water demand
simulated by the crop module and the streamflow simulated by the river
routing and reservoir operation modules finally enter into the withdrawal
module. Note that the crop module is independent, except for the water
stress calculations, which require evapotranspiration and potential
evapotranspiration inputs from the land surface hydrology module.
Schematic diagram showing the six submodules (a) and basic
biophysical processes of the crop module (b) in the H08 model.
Figure 1b shows the basic biophysical process of the crop module in H08. The
biomass accumulation is based on Monteith et al. (1977). The crop phenology
development is based on daily heat unit accumulation theory. The harvest
index is used to partition the grain yield. Regulating factors, including
water and air temperature, are used to constrain the yield variation. The
crop module can simulate the potential yield, crop calendar, and irrigation
water consumption for 18 crops, including barley, cassava, cotton, peanut,
maize, millet, oil palm, potato, pulses, rape, rice, rye, sorghum, soybean,
sugar beet, sugarcane, sunflower, and wheat. The parameters for these crops
were taken from those of the SWAT model. To better reflect the agronomy
practice, H08 divides each simulation cell into four sub-cells: rainfed,
single-irrigated, double-irrigated, and other (i.e., non-agricultural land
uses). Irrigation in H08 is defined as the supply of water other than
precipitation to maintain soil moisture above 75 % of field capacity
during the cropping period. To clarify this as regards the function of the
parameters we calibrated below, here we describe the algorithms in the crop
module of H08. The crop module of H08 accumulates daily heat units
(Huna(t)), which are expressed as the daily mean air temperature (Ta) greater
than the plant's specific base temperature (Tb; given as a crop-specific
parameter):
Hunat=Ta-Tb.
Then the heat unit index (Ihun) is calculated as the ratio of accumulated
daily heat units ∑Hunat and the potential heat unit
(Hun):
Ihun=∑HunatHun.
When the accumulated daily heat units ∑Hunat reach
the potential heat unit (Hun) required for the maturity of the crop, the
crop is mature and is harvested. During the growth period, the daily
increase in biomass (ΔB) is calculated using a simple photosynthesis
model:
ΔB=be⋅PAR⋅REGF,
where be is radiation use efficiency, PAR is photosynthetically active
radiation, and REGF is the crop regulating factor. PAR is calculated
using shortwave radiation (Rs) and leaf area index (LAI) as follows:
PAR=0.02092⋅Rs⋅[1-exp(-0.65⋅LAI)].
LAI is calculated according to the growth stage indicated by Ihun; if
Ihun <⌊dpl1⋅0.01⌋,
LAI=dpl1-dpl1⋅Ihundpl1⋅0.01⋅blai);
if ⌊dpl1⌋⋅0.01≤Ihun<dpl2⋅0.01,
LAI=dpl1-dpl1+dpl2-dpl2-dpl1-dpl1⋅Ihun-dpl1⋅0.01dpl2⋅0.01-dpl1⋅0.01⋅blai;
if ⌊dpl2⌋⋅0.01≤Ihun<dlai,
LAI=dpl2-dpl2+1-dpl2-dpl2⋅Ihun-dpl2⋅0.01dlai-dpl2⋅0.01⋅blai;
if dlai < Ihun
LAI=16⋅blai(1-Ihun)2,
where dpl1 and dpl2 are two complex numbers (see the definition in Table 1) and blai is the maximum leaf area index.
Parameter abbreviations and explanation.
ParameterFull namePhysical meaningabbreviationHunPotential heat unitThe value of potential heat units required for the maturity of the cropbeRadiation use efficiencyThe potential growth rate per unit of intercepted photosynthetically active radiationToOptimum temperatureThe optimal temperature for plant growthTbBase temperatureThe base temperature for plant growthblaiMaximum leaf area indexThe maximum potential leaf area indexdlaiFraction of growing season when growthdeclinesSame as the full namedpl1Complex number1First point on the optimal leaf area development curve. Before decimal: fraction of growing season; after decimal: max corresponding LAI.dpl2Complex number2Before decimal: fraction of growing season; after decimal: max corresponding LAI. Second point on the optimal leaf area development curve.rdmxMaximum rooting depthSame as the full nameHunmaxMaximum daily accumulation of temperatureSame as the full nameTSAWMinimum temperature for plantingSame as the full name
REGF is calculated as
REGF=min(Ts,Ws,Ns,Ps),
where Ts, Ws, Ns, and Ps are the respective stress factors for
temperature, water, nitrogen, and phosphorous. Temperature stress (Ts) is
calculated as an asymmetrical function according to the relationship between
air temperature (Ta) and optimal temperature (To). When air temperature
is below (or equal) the optimal temperature (To), Ts is calculated as
Ts=expln(0.9)⋅Ctsl(To-Ta)Ta2,
where Ctsl is the temperature stress parameter for temperature below To and is
calculated as
Ctsl=To+TbTo-Tb.
When air temperature is above the optimal temperature, Ts is calculated
as
Ts=expln(0.9)⋅(To-Ta)Ctsh2,
where Ctsh is the temperature stress parameter for temperature below To and is
calculated as
Ctsh=2⋅To-Ta-Tb.
Water stress (Ws) is calculated as the ratio of actual evapotranspiration
(Ea) to potential evapotranspiration (Ep) as
Ws=EaEp.
The crop yield (Yld) is finally estimated from the aboveground biomass
(Bag) using the crop-specific harvest index (Harvest) at the harvesting
date as
15Bag=[1-(0.4-0.2⋅Ihun)∑ΔB,16Yld=Harvest⋅WSFWSF+exp(6.117-0.086⋅WSF)⋅Bag,
where WSF is the ratio of SWU (the accumulated actual plant
evapotranspiration in the second half of the growing season) and SWP (the
accumulated potential evapotranspiration in the second half of the growing
season):
WSF=SWUSWP⋅100.
Enhancement of H08 for Miscanthus and switchgrass
The original bioenergy crop implementation in H08 (Yamagata et al., 2018)
was conducted in two steps. First, crop parameters (see the old values in
Table 2) for Miscanthus (refer to Miscanthus giganteus in this study) and switchgrass (refer to Panicum virgatum in this
study) were adopted based on the settings from the SWAT model 2012 version
(Arnold et al., 2013). However, the default parameters did not reflect the
characteristics for Miscanthus and switchgrass well, which could lead to serious bias
based on the result in Trybula et al. (2015). Second, maturity was defined
by either undergoing an autumn freeze (i.e., the air temperature was below
the minimum temperature for growth) or the exceedance of the maximum of 300
continuous days of growth. Because both Miscanthus and switchgrass are perennial, the
potential heat unit was set as unlimited (see the old values in Table 2).
However, this unlimited potential heat unit is far from the observations
(see the new values in Table 2) reported by Trybula et al. (2015). Here,
further enhancements were made as follows. First, we changed the leaf area
development curve by adopting the potential heat unit (Hun) and leaf-area-related parameters (dpl1 and dpl2) proposed by Trybula et al. (2015). The
potential heat unit can determine both the total cropping days and the leaf
development. Here, we set the values at 1830 and 1400∘ for
Miscanthus and switchgrass, respectively, as recommended by Trybula et al. (2015)
based on their field observations. The dpl1 and dpl2 parameters (see Table 1), which were used for determining the leaf development curve, were also
changed to the values suggested by Trybula et al. (2015). This modification
substantially changed the original heat unit index (Ihun) and the
development of the leaf area index curve. Second, we modified the algorithm
for water stress that was used to regulate the radiation use efficiency. We
took the ratio of actual evapotranspiration to potential evapotranspiration
as the water stress factor for any point in the simulation, similar to the
description of the soil moisture deficit used in other studies (Anderson et
al., 2007; Yao et al., 2010). Third, we added a new output variable for the
water consumption of Miscanthus and switchgrass to analyze the water consumption and
WUE in the crop submodule. Fourth, we introduced the Köppen climate
classification (see Fig. 2) into the source code to provide possible
climate-specific analyses. Finally, we conducted parameter calibrations with
the best available data. The calibration process is presented below, and the
finalized parameter settings are given in Table 2.
Parameters set in the enhanced H08 model.
Bioenergy cropParameterOld valueNew valueSourceMiscanthusHun99991830Trybula et al. (2015)be3938CalibratedTo3025Trybula et al. (2015); Hastings et al. (2009)Tb108Calibratedblai11.511Calibrateddlai0.851.1Trybula et al. (2015)dpl110.210.1Trybula et al. (2015)dpl250.9545.85Trybula et al. (2015)rdmx43Trybula et al. (2015)Hunmax12.511.5CalibratedTSAW10.08.0CalibratedSwitchgrassHun99991400Trybula et al. (2015)be4722CalibratedTo2525Trybula et al. (2015)Tb1210Calibratedblai68Calibrateddlai0.71Trybula et al. (2015)dpl110.210.1Trybula et al. (2015)dpl220.9540.85Trybula et al. (2015)rdmx2.23Trybula et al. (2015)Hunmax12.515.5CalibratedTSAW10.08.0Calibrated
Map showing the locations of the Miscanthus (red dots) and switchgrass (blue
dots) sites under rainfed condition and the Köppen climate zones.
We conducted a calibration with five important parameters, the radiation use
efficiency (be), maximum leaf area index (blai), base temperature (Tb),
maximum daily accumulation of temperature (Hunmax), and minimum temperature
for planting (TSAW). The specific parameter ranges and steps set in the
calibration process are shown in Table 3. In total, 1944 simulations were
conducted for Miscanthus and switchgrass to test all combinations of the parameter
sets. The simulations were conducted with the averaged daily meteorology
data from WATCH-Forcing-Data–ERA-Interim (WFDEI) (1979–2016) for two reasons. First, using multi-year
averaged metrology input can exclude the effect of extreme climate (low
temperatures in early spring and late autumn) on the yield, and this is
recommended in the H08 manual (Hanasaki and Yamamoto, 2010). Second, it can
largely save the computation storage. The best parameter sets were selected
using two steps: first, the lowest root mean square error (RMSE) and, second, the highest correlation coefficient (R) of the simulated and
observed yields within the lowest RMSE domain. Additional information on how
these parameters affect the model can be found in the equations described in
Sect. 2.1.
Parameter ranges for the calibration simulations.
Bioenergy cropParameterRangeIncrementUnitReferenceMiscanthusbe(30, 40)2g MJ-1×10Clifton-Brown et al. (2000); van der Werf et al. (1992); Beale and Long (1995); Heaton et al. (2008); Trybula et al. (2015) blai(9, 11)1m2 m-2Heaton et al. (2008); Trybula et al. (2015)Tb(7, 9)1∘Beale et al. (1996); Trybula et al. (2015)Hunmax(11.5, 16.5)1∘H08 endogenous variableTSAW(8, 10)1∘H08 endogenous variableSwitchgrassbe(12, 22)2g MJ-1×10Heaton et al. (2008); Madakadze et al. (1998); Trybula et al. (2015)blai(6, 8)1m2 m-2Trybula et al. (2015); Giannoulis et al. (2016); Madakadze et al. (1998); Heaton et al. (2008)Tb(8, 10)1∘Trybula et al. (2015)Hunmax(11.5, 16.5)1∘H08 endogenous variableTSAW(8, 10)1∘H08 endogenous variableModel input data
The WFDEI global meteorological data
(Weedon et al., 2014) from 1979 to 2016 were used in all simulations.
The WFDEI data were based on the methodology used for WATer and global
CHange (WATCH) forcing data by utilizing ERA-Interim global reanalysis data.
The data cover the whole globe at a spatial resolution of 0.5∘.
Eight daily meteorological variables (air temperature, wind speed, air
pressure, specific humidity, rainfall, snowfall, and downward shortwave and
longwave radiation) were used to run H08. Another meteorological dataset for
the period 1979–2013 in S14FD (Iizumi et al., 2017) with the same spatial
resolution was also used to check the stability of results to input
meteorological data.
Yield data
To independently calibrate and validate the performance of H08 in simulating
the bioenergy yield, we collected and compiled up-to-date site-specific
(varied from 1986 to 2011) and country-specific (varied from 1960 to 2010)
yield data from both observations and simulations (Clifton-Brown et al.,
2004; Searle and Malins, 2014; Heck et al., 2016; Kang et al., 2014; Li et
al., 2018a). For Miscanthus, the yield data used covered 72 sites (64 rainfed and 8
irrigated; observed) and 15 countries (simulated). The simulated
country-specific data are from MISCANMOD and LPJmL. For switchgrass, the
yield data used covered 57 sites (55 rainfed and 2 irrigated; observed) and
16 countries (simulated). The simulated country-specific data are from
HPC-EPIC and LPJmL. A map showing the locations of the majority of sites
under the rainfed condition and the corresponding climate zone is presented
in Fig. 2. The data sites were predominantly distributed in Europe and the
US. It should be noted that the sites are generally located in temperate and
continental climate zones, with few located in the tropics and dry climate
zones. Detailed lists of the sites from which the yields of Miscanthus and switchgrass
were reported are documented in Tables S1 and S2 (for the rainfed condition)
and Table S3 (for the irrigated condition) in the Supplement.
A global yield map of Miscanthus and switchgrass that was generated using a
random-forest algorithm (Li et al., 2020) was also used to compare the
results. This yield map provides a benchmark for evaluating model
performance because it is largely constrained by the observed yield ranges,
denoting the yields achievable under current technologies (Li et al., 2020).
Simulation setting
After calibration, four different kinds of simulation were run with
different purposes. The first simulation was conducted using the original
model without irrigation to investigate its performance. The second
simulation was conducted using the enhanced model without irrigation to
investigate its performance under rainfed condition. The third simulation
was conducted using the enhanced model with irrigation to investigate its
performance under irrigated condition. These three simulations were
conducted at a daily scale with annual meteorological data from WFDEI for
the period 1979–2016. The last simulation was conducted using identical
model settings to the third one, except using different meteorological data
from S14FD for the period 1979–2013. Note that irrigation in this study
means uniform unconstrained irrigation.
Water use efficiency
WUE is an important indicator that shows the
efficiency of crops in using water to produce biomass (Ai et al., 2020),
which is useful in evaluating bioenergy crop performance (Zeri et al.,
2013). Here, WUE is calculated as the ratio of yield to water consumption:
WUE=yieldwater consumption,
where yield and water consumption refer to the bioenergy crop yield (kg ha-1 yr-1) and the corresponding water consumption (mm yr-1)
of Miscanthus and switchgrass.
Sensitivity analysis
To see the sensitivity of the calibrated variables to the yield simulation,
we calculated the sensitivity index (S) (Cheng et al., 2020) value for each
variable:
S=∑(Vs-Vref)/Vref(Ps-Pref)/Pref‾,
where Vs and Vref are the calculated RMSE of the simulated and
observed yields for the corresponding calibration simulations and the
finalized simulation (with final fixed parameters in Table 2) and Ps and
pref are the parameter values for the corresponding calibration
simulations and the finalized simulation.
Results and discussionParameter calibration
The variation in RMSE and R for all 1944 simulations is presented in Fig. S1. Both RMSE and R have large ranges. Based on the optimal values of RMSE
(4.68 and 3.16 Mg ha-1 yr-1 for Miscanthus and switchgrass, respectively)
and R (0.67 and 0.53 for Miscanthus and switchgrass, respectively), we finalized the
parameter set as shown in Table 2. The simulations presented in the table
are for rainfed conditions because only a few sites were irrigated. The
radiation use efficiency values were set at 38 and 22 (g MJ-1×10) for Miscanthus and switchgrass, respectively. These values are similar
to those of Trybula et al. (2015), who recommended values of 41 (g MJ-1×10) for Miscanthus and 17 (g MJ-1×10) for switchgrass. The
base temperatures were calibrated to be 8 and 10 ∘C for
Miscanthus and switchgrass, respectively. The base temperature is sensitive to the
crop growing days. Ranges from 7 to 10 ∘C for Miscanthus and from 8 to
12 ∘C for upland switchgrass were suggested by Trybula et al. (2015). The calibrated values are within the above ranges. The maximum leaf
area indices were calibrated at 11 and 8 for Miscanthus and switchgrass, respectively;
these values were identical to those suggested by Trybula et al. (2015). Of
the five parameters we calibrated, radiation use efficiency was the most
sensitive parameter to the result, followed by the base temperature (see
Table S5); this is consistent with the result of Trybula et al. (2015). As
shown in Fig. 3, the calibrated parameters performed well, since the scatter
points are well distributed along the 1:1 line.
Overall comparison of the calibrated (Cal.) and observed (Obs.)
yields for Miscanthus and switchgrass. The black line is the 1:1 line.
Site-specific performance of enhanced H08
An overview of the performance of the enhanced H08 is provided in Fig. 4.
The simulated yield is the annual average from 1986 to 2011. Points in a
scatterplot comparing simulated yields derived from the enhanced H08 with
observed yields are well distributed along the 1:1 line. It can be seen that
the performance of the enhanced H08 was improved over that of the original
H08. For Miscanthus, the bias of the original model ranged from -84 % to 80 % with a
mean of -52 %, while the bias of the enhanced model ranged from -59 %
to 53 % with a mean of -9 %. For switchgrass, the bias for the original
model ranged from -78 % to 338 % with a mean of 25 %, while the bias
for the enhanced model ranged from -52 % to 109 % with a mean of
-7 %. Note that Fig. 4 also shows a tendency toward underestimation for some
sites, especially for Miscanthus. More detailed site-specific results are shown in
Fig. 5a (Miscanthus) and Fig. 5b (switchgrass). To depict the uncertainties in the
observed yield, the minimum and maximum observed yields are shown as error
bars in Fig. 5. It was found that the simulated yields were within or close
to the range of the observed yields. The simulated relative error was
randomly distributed, was substantially smaller than the range of the
observed yields, and showed no climatic bias. This implies that the
combination of the Hun identified by Tryubula et al (2015) and the
calibrated parameters of this study are valid for climate zones other than
that of the midwestern US, where the Hun was observed. We also investigated
the performance under irrigated conditions (shown in Fig. 6). We used the
reported observed yields for 10 sites globally (Table S3). We found that
the simulated yields were within or close to the observed yields for five
sites located in China, the UK, and France (see Table S3) but were
overestimated for the remaining sites. This was due to the assumption of
irrigation. H08 assumes that irrigation is fully applied to crops and hence
the yield represents the maximum potential yield under irrigation condition.
Therefore, if the reported yield is within the range of the simulated yield
between rainfed and irrigated conditions, it is considered reasonable. This
was found to be the case, as shown in Fig. 6. To investigate the uncertainty
in the meteorological data, a simulation using other meteorological data
from the S14FD dataset (Iizumi et al., 2017) was conducted; the results are
compared in Fig. S2. The comparison showed that the WFDEI-driven result was
very similar to that obtained with the S14FD data.
Overall comparison of the simulated (Sim.) and observed (Obs.) yields
for Miscanthus and switchgrass. The simulated yields in (a) and (b) are from the
original H08 model, whereas those in (c) and (d) are from the enhanced H08
model. The black line is the 1:1 line.
Site-specific performance (presented with latitude increasing from
the bottom of the vertical axis) and relative error of the simulated yield
obtained using the enhanced H08 model compared with the observed yields for
Miscanthus and switchgrass. The longitude and latitude of each location for
Miscanthus and switchgrass are given in Tables S1 and S2, respectively. The “x” indicates the site's climate, where 1, 2, 3, and 4 refer to tropical,
dry, temperate, and continental climate zones, respectively. Obs. means the
observed mean yield. The black error bar represents the range of the
observed minimum and maximum yield. The red or blue error bar represents the
range of the simulated minimum and maximum yield.
Site-specific performance (shown with increasing latitude from the
bottom of the vertical axis) of the simulated yield (sim.) obtained using
the enhanced H08 model compared with observed yields (obs.) for Miscanthus (mis.) and
switchgrass (swc.) under irrigated condition. The longitude and latitude of
each location ID for Miscanthus and switchgrass are given in Table S3. Obs. indicates
the observed mean yield. The black error bar represents the range of the
observed minimum and maximum yield. The red or blue error bar represents the
range of the simulated minimum and maximum yield.
Country-specific performance of enhanced H08
Figure 7 compares the yield simulated by the enhanced H08 with the collected
independent country-specific yields simulated by MISCANMOD (Clifton-Brown et
al., 2004), HPC-EPIC (Kang et al., 2014), and LPJmL (Heck et al., 2016).
Here, the yield was simulated under rainfed conditions. The periods of
climate data used as inputs were 1960–1990, 1980–2010, and 1982–2005 for
MISCANMOD, HPC-EPIC, and LPJmL, respectively. Here, the comparisons were
conducted using exactly the same period as that of HPC-EPIC and LPJmL. For
MISCANMOD, however, we used the data from 1979–1990 due to data
availability. For Miscanthus, the correlation coefficient of the yield simulated by
H08 and MISCANMOD in the scatterplot (Fig. 7d) was 0.40. A t test showed
that the correlation was not significant at the 0.01 level. For consistency
with the yield collected by MISCANMOD, any area within a country where the
yield was less than 10 Mg ha-1 yr-1 was excluded from the
analyses. Also, the land available for calculations was set as 10 % of the
pastureland and cropland. For switchgrass, the correlation coefficient of the
yield simulated by H08 and HPC-EPIC in the scatterplot (Fig. 7e) was 0.80. A
t test showed that the correlation was significant at the 0.01 level. This
indicates that the spatial pattern of the yield simulated by H08 was similar
to that of HPC-EPIC. For example, both models produced high yields in
Brazil, Colombia, Mozambique, and Madagascar, and low yields were found in
Australia and Mongolia.
An independent country-specific comparison of the yield simulated by
the enhanced H08 model with those of three other models (MISCANMOD,
HPC-EPIC, and LPJmL) for Miscanthus(a, d), switchgrass (b, e), and their combination
(c, f). The H08 in (c, f) indicates the average yield of Miscanthus and switchgrass,
and the upper and lower error bars in (c) represent the yields for
Miscanthus and switchgrass, respectively.
Miscanthus and switchgrass are not distinguished in LPJmL, and we therefore compared
the mixed (mean of Miscanthus and switchgrass) yield of Miscanthus and switchgrass simulated by H08
and the C4 grass yield simulated by LPJmL. The correlation coefficient
of the yield simulated by H08 and LPJmL in the scatterplot (Fig. 7f) was
0.77. A t test showed that the correlation was significant at the 0.01
level. An additional comparison under the irrigated condition is presented
in Fig. S3. The correlation coefficient of the yield simulated by H08 and
LPJmL, as shown in the scatterplot (Fig. S3), was 0.95. A t test showed that
the correlation was significant at the 0.01 level. The difference was mainly
due to Colombia, Sudan, Mozambique, and Mexico, which are located in
tropical zones. The difference in these countries was generally equal to the
range of H08. For example, as shown in Fig. 7c, the yield in Colombia
simulated by LPJmL was equal to the Miscanthus yield simulated by H08 (upper error
bar). A separate comparison of the ensemble yield simulated by LPJmL, and
the yield of Miscanthus and switchgrass simulated by H08 under both rainfed and
irrigated conditions is presented in Fig. S4. It can be seen that the yield
of Miscanthus simulated by H08 was closer to the yield simulated by LPJmL, which
indicates that the LPJmL-simulated yield was more likely to represent
Miscanthus. This can also be inferred from the validation results (Fig. 1a) in Heck et
al. (2016) since the LPJmL-simulated yield is close to the yield of
Miscanthus compared to those of switchgrass. It was difficult to determine which model
performed better due to the lack of observed data in tropical zones. This
also indirectly indicated the relatively large uncertainty of the existing
simulations in tropical zones (Kang et al., 2014).
The differences in model structure, the use of specific algorithms, and the
input climate data (different periods and sources) can induce differences in
the yield simulated by MISCANMOD, HPC-EPIC, LPJmL, and H08. With regard to
model structure, MISCANMOD uses a Kriging interpolation method to derive the
spatial yield from the original site yield, whereas H08, LPJmL, and HPC-EPIC
use grid-based calculations. H08 considers the single-harvest system in
tropical areas, whereas LPJmL considers a multiple-harvest system. With
regard to the specific algorithms used, the water stress used to regulate
radiation use efficiency varies considerably among the models. Note that the
differences in meteorological data sources and spatial–temporal resolution
would also contribute to these differences.
Further evaluation of the performance of enhanced H08
As shown in Fig. 8, we compared our simulation with the latest available
global bioenergy crop yield map, generated from observations using a
random-forest (RF) algorithm (Li et al., 2020). As shown in Fig. 8a and b, there were
small differences between our estimated yield and RF yield for switchgrass,
whereas larger differences were found for Miscanthus, especially in tropical regions.
There is a similar case for ORCHIDEE, as shown in Fig. S21 in Li et al. (2020). We also compared the differences in the mean values for Miscanthus and
switchgrass because they are not distinguished in LPJmL. As shown in Fig. 8c
and d, the differences between our estimations and the RF yields were
generally lower than those between the LPJmL estimations and RF yields. In
summary, our estimations were well within the ranges of those of ORCHIDEE
and LPJmL.
Comparison of the yield difference (simulated yields minus RF yields)
between model simulations and the RF map (Li et al., 2020): (a) for
Miscanthus with the yield from H08 minus that from RF; (b) for switchgrass with the yield from H08
minus that from RF; (c) for the mean of Miscanthus and switchgrass with the yield from
H08 minus that from RF; (d) the ensemble yield of Miscanthus and switchgrass with the
yield from LPJmL minus that from RF. The units for the legends are Mg ha-1 yr-1.
Spatial distributions of the simulated yield under rainfed and irrigated
conditions
Figure 9 shows the global yield distributions of Miscanthus and switchgrass. Under
rainfed conditions, high yields are distributed in the eastern US, Brazil,
southern China, Africa, and Southeast Asia. To evaluate the response of
yield to irrigation, we compared the results under rainfed and irrigated
conditions. As shown in Fig. 9c and d, unconstrained irrigation greatly
increased yields, especially of areas in arid regions such as the western
US, southern Europe, northeastern China, India, southern Africa, the Middle
East, and coastal Australia. At the global scale, the increases (excluding
the area with a polar climate) were 20.7 (from 16.8 to 37.5) Mg ha-1 yr-1 and 7.9 (from 7.4 to 15.3) Mg ha-1 yr-1 for Miscanthus and
switchgrass, respectively, indicating that irrigated yield was more than
double the rainfed yield. The spatial distributions of yield increases due
to irrigation simulated by H08 were very similar to those simulated by LPJmL
(Beringer et al., 2011). At the continental scale (e.g., Europe), yield
increases were located mainly in southern Europe, consistent with the
findings obtained using MISCANMOD (Clifton-Brown et al., 2004). The response
of yield to irrigation was weaker for switchgrass than for Miscanthus (see Fig. 9b
and d). This might have been due to switchgrass having less dependency on
water compared to Miscanthus (Mclsaac et al., 2010). Miscanthus growth has been reported to have
a high water requirement due to its high yield, large leaf area index, and
long growing season (Mclsaac et al., 2010; Lewandowski et al., 2003). As a
result, Miscanthus yield is strongly influenced by water availability, and an annual
rainfall of 762 mm yr-1 is thought to be suitable for growth
(Heaton et al., 2019). However, the precipitation in most locations is
below this level, especially in arid and semi-arid regions (see Fig. S5 in
the Supplement). Therefore, irrigation plays a critical role in
ensuring optimum bioenergy crop yield in arid and semi-arid regions,
especially for Miscanthus.
Spatial distributions of the simulated yields (exceeds 2 Mg ha-1 yr-1) for Miscanthus(a, c) and switchgrass (b, d) under rainfed (a, b) and
irrigated (c, d) conditions. The units for the legends are Mg ha-1 yr-1.
Effects of irrigation on yield, water consumption, and WUE in different
climate zones
Climate is one of the main physical constraints of crop growth and yield.
Figure 10a shows the mean yield for Miscanthus and switchgrass in four different
Köppen climate zones (see Fig. S6 in the Supplement). For
Miscanthus, a tropical climate (including the northern part of South America, central
Africa, Southeast Asia, and southern India) produced the highest average
yield of 33.0 Mg ha-1 yr-1. A temperate climate (including the
eastern US, Europe, southern China, and the southern part of South America)
produced the second-highest average yield of 19.7 Mg ha-1 yr-1.
Dry and continental climate zones had similar average yields of 8.3 and 6.2 Mg ha-1 yr-1, respectively. For switchgrass, a tropical climate
had the highest yield, averaging 11.9 Mg ha-1 yr-1. For the other
three climate types, the average yields averaged 9.0, 4.7, and 4.0 Mg ha-1 yr-1 for the temperate, continental, and dry climate zones,
respectively. As shown in Fig. 10a, irrigation greatly increased the yield,
especially in dry climate zones, which had the largest yield increases of
44.2 and 15.7 Mg ha-1 yr-1 for Miscanthus and switchgrass, respectively. In
contrast, irrigation had a relatively weak effect on yield in the tropical
climate zone.
Variation in the average yield (a), crop water consumption (b), and
water use efficiency (WUE) (c) of Miscanthus and switchgrass under rainfed and
irrigated conditions in four different Köppen climate zones (tropical,
dry, temperate, and continental climates) based on meteorology data
collected from 1979 to 2016. The abbreviations M. and S. in the legend denote
Miscanthus and switchgrass, respectively.
Figure 10b shows the water consumption for both Miscanthus and switchgrass. The annual
mean water consumption of Miscanthus was around 613 mm yr-1 for the tropical
climate zone (with a high yield of 33.0 Mg ha-1 yr-1), whereas it
was 155 mm yr-1 for a dry climate (with a low yield of 8.3 Mg ha-1 yr-1) under rainfed conditions. Under irrigated conditions, the largest
increases in water consumption were 1618 and 1054 mm yr-1 for
Miscanthus and switchgrass in dry climate zones, respectively. With such large amounts
of irrigation, the yield in a dry climate zone can exceed that in a tropical
climate zone under rainfed conditions. This highlights the yield–water
tradeoff effects.
As shown in Fig. 10c, the WUE values of Miscanthus in a tropical climate were 53.8 kg DM (dry matter) ha-1 mm-1 H2O and 53.5, 48.2, and 47.0 kg DM ha-1 mm-1 H2O, respectively, in dry, temperate, and continental climate
zones under rainfed conditions. The respective WUE values of switchgrass were
41.2, 37.9, 30.4, and 29.7 kg DM ha-1 mm-1 H2O in
continental, dry, tropical, and temperate climate zones under rainfed
conditions. The WUE values for Miscanthus were higher than those for switchgrass,
which is inconsistent with values in previous reports (Van Loocke et al.,
2012). With irrigation, the WUE decreased for both Miscanthus and switchgrass in all
climate zones. Globally, excluding the area with a polar climate, the
decreases were 14.2 (from 50.6 to 36.4) kg DM ha-1 mm-1 H2O
and 12.2 (from 34.8 to 22.6) kg DM ha-1 mm-1 H2O for
Miscanthus and switchgrass, respectively, indicating a reduction in the mean WUE values
for Miscanthus and switchgrass of up to 32 %. This is consistent with the current
global WUE trend for crops, which is high for rainfed croplands but low for
irrigated croplands. However, the general magnitude of this relationship
changes if the site or regional scale is considered based on reports for
wheat in Syria (Oweis et al., 2000) or for wheat and maize in the North
China Plain (Mo et al., 2005). Note that it might be better to use a
specific crop model to investigate WUE at the site or watershed scale.
Improvements and limitations
Compared with earlier studies, our study made several important
improvements. First, rather than using an approximation for C4 grass to
represent Miscanthus and switchgrass in the LPJmL model, our enhanced H08 model
simultaneously simulated the yields of Miscanthus and switchgrass at the global scale.
Compared with the original H08, our enhanced model markedly decreased the
mean bias (from -52 % to -9 % for Miscanthus and from 25 % to -7 % for switchgrass).
Moreover, the growing seasons for Miscanthus (145–165) and switchgrass (101–114)
during the period 2009–2011 at the Water Quality Field Station of the
Purdue University Agronomy Center are consistent with the values of 140 and
120 reported in Trybula et al. (2015). Second, the hydrological effects of
bioenergy crop production implemented in our model are actually not
incorporated in some other models; for example, we considered irrigation and
analyzed WUE, which was not implemented in ORCHIDEE-MICT-BIOENERGY (Li et
al., 2018b) and HPC-EPIC (Kang et al., 2014). Third, we investigated the
differences in yield, water consumption, and WUE of both Miscanthus and switchgrass
among different climate zones, which was useful for bioenergy land-scenario
design. For example, more land can be allocated to the areas with greater
WUE. In summary, our enhanced model provides a new tool that can
simultaneously simulate Miscanthus and switchgrass with a consideration of water
management (such as irrigation), although it currently considers only
herbaceous bioenergy crops. From this perspective, we firmly believe that
our enhanced model contributes to the bioenergy crop modeling community.
There are still several uncertainties and limitations that need to be
addressed in the future. First, the bioenergy crop yield simulated by H08
did not include constraints due to nutrients, such as nitrogen and
phosphorus. Nutrient dynamics are influenced by complex site-specific soil
conditions (soil type, temperature, wetness, carbon, etc.), which remain
quite challenging to represent properly in global models. This is why
similar assumptions and limitations occur in the latest bioenergy
potential or yield studies (Li et al., 2018b; Yamagata et al., 2018; Wu et al.,
2019). Second, the effects of CO2 fertilizer and technological
advancements were not considered in the current simulations. Third, our
simulation was conducted with historical meteorological drivers. Therefore,
yield variations in future climate scenarios under different representative
concentration pathways need to be examined. Fourth, the current irrigation
levels were input to represent uniform unconstrained irrigation. Further
evaluations need to consider the availability of renewable water sources
and planetary boundaries of land, food, and water (Heck et al., 2018).
Finally, as with other models, like MISCANMOD (Clifton-Brown et al., 2004),
SWAT (Neitsch et al., 2011), and LPJmL (Bondeau et al., 2007), we adopted a
crop-uniform water stress formulation. However, an earlier study indicated
that the water stress could be crop-specific (Hastings et al., 2009).
Additional investigations of the water stress formulation for different
bioenergy crops are needed.
Conclusions
In this study, we enhanced the ability of the H08 global hydrological model
to simulate the yield of the dedicated second-generation herbaceous
bioenergy crops. The enhanced H08 model generally performed well in
simulating the yield of both Miscanthus and switchgrass, with the estimations being
well within the range of observations and other model simulations. To the
best of our knowledge, this study is the first attempt to enable a global
hydrological model with a consideration of water management, such as
irrigation, to simulate the yield of Miscanthus and switchgrass separately. The
enhanced model could be a good tool for the future assessments of the
bioenergy–water tradeoffs. Using this tool, we quantified the effects of
irrigation on yield, water consumption, and WUE for both Miscanthus and switchgrass in
different climate zones. We found that irrigation more than doubled the
yield in all areas under rainfed conditions and reduced the WUE by 32 %.
However, due to the low water consumption in tropical areas, the highest WUE
was generally found in tropical climate zones, regardless of whether the
crop was irrigated.
Code and data availability
The code of the model used in this study is archived on Zenodo
(10.5281/zenodo.3521407, Ai et al., 2019) under the Creative
Commons Attribution 4.0 International License. Technical information about
the H08 model and the input dataset is available from the following
website: http://h08.nies.go.jp (last access: 1 February 2020).
The supplement related to this article is available online at: https://doi.org/10.5194/gmd-13-6077-2020-supplement.
Author contributions
NH and ZA designed this study. ZA collected the data, developed
the model code, and performed the simulations. VH provided simulation data from LPJmL. ZA prepared the
paper, with contributions and comments from NH, VH,
TH, and SF.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
The authors would like to acknowledge Wei Li and Liyin He for their suggestions for this work.
Financial support
This study was supported by the Environment Research and Technology Development Fund (grant nos. JPMEERF20202005 and JPMEERF15S11418) of the Environmental Restoration and Conservation Agency, Japan.
Review statement
This paper was edited by Hisashi Sato and reviewed by two anonymous referees.
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